Abstract

Background

The advent of RNA interference techniques enables the selective silencing of biologically
interesting genes in an efficient way. In combination with DNA microarray technology
this enables researchers to gain insights into signaling pathways by observing downstream
effects of individual knock-downs on gene expression. These secondary effects can
be used to computationally reverse engineer features of the upstream signaling pathway.

Results

In this paper we address this challenging problem by extending previous work by Markowetz
et al., who proposed a statistical framework to score networks hypotheses in a Bayesian
manner. Our extensions go in three directions: First, we introduce a way to omit the
data discretization step needed in the original framework via a calculation based
on p-values instead. Second, we show how prior assumptions on the network structure can
be incorporated into the scoring scheme using regularization techniques. Third and
most important, we propose methods to scale up the original approach, which is limited
to around 5 genes, to large scale networks.

Conclusion

Comparisons of these methods on artificial data are conducted. Our proposed module
network is employed to infer the signaling network between 13 genes in the ER-α pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction
can be found with good statistical stability.

The code for the module network inference method is available in the latest version
of the R-package nem, which can be obtained from the Bioconductor homepage.